Survey-Based Cross-Country Comparisons Where Countries Vary in Sample Design: Issues and Solutions

Abstract

Abstract In multi-national surveys, different countries usually implement different sample designs. The sample designs affect the variance of estimates of differences between countries. When making such estimates, analysts often fail to take sufficient account of sample design. This failure occurs sometimes because variables indicating stratification, clustering, or weighting are unavailable, partially available, or in a form that is unsuitable for cross-national analysis. In this article, we demonstrate how complex sample design should be taken into account when estimating differences between countries, and we provide practical guidance to analysts and to data producers on how to deal with partial or inappropriately-coded sample design indicator variables. Using EU-SILC as a case study, we evaluate the inverse misspecification effect (imeff ) that results from ignoring clustering or stratification, or both in a between-country comparison where countries’ sample designs differ. We present imeff for estimates of between-country differences in a number of demographic and economic variables for 19 European Union Member States. We assess the magnitude of imeff and the associated impact on standard error estimates. Our empirical findings illustrate that it is important for data producers to supply appropriate sample design indicators and for analysts to use them.